of the Diploma Thesis In this Diploma Thesis, the electrochemical behavior of 4-nitrophenol (4-NP) was studied at a newly prepared large-surface carbon film electrode (ls-CFE) using techniques of DC voltammetry (DCV) and differential pulse voltammetry (DPV) with the aim to develop sensitive analytical methods for its determination. Voltammetric behavior of 4-NP was investigated in the region of anodic potentials, in dependence on the pH of the medium used (realized using Britton-Robinson buffer). The optimum pH values for the determination of 4-NP were chosen as follows: pH 3.0 (for DCV) and pH 7.0 (for DPV). During anodic oxidation of 4-NP on the ls-CFE at the concentration of the analyte of 1·10-4 mol/l, the passivation of the electrode surface occurred. Thus, it was decided to record series of measurements always at a new carbon film. Using the sample of 4-NP (at the concentration of 1·10-4 mol/l), the repeatability of the application of individual carbon films was tested, with obtained RSD values of 3.7% and 3.6% for DCV and DPV, respectively. Under optimum conditions, the calibration dependences of 4-NP were measured in the concentration range from 1·10-6 to 1·10-4 mol/l, with the limits of quantification (LQs) of 1.5·10-6 mol/l (for DCV at the ls-CFE) and 4.6·10-7 mol/l (for DPV at the...

Submitted by Biblioteca de Pós-Graduação em Geoquímica BGQ (bgq@ndc.uff.br) on 2017-10-03T16:45:06Z No. of bitstreams: 1 Tese completa.pdf: 986993 bytes, checksum: 786c2673f26a627f8b28edd297319032 (MD5) / Made available in DSpace on 2017-10-03T16:45:06Z (GMT). No. of bitstreams: 1 Tese completa.pdf: 986993 bytes, checksum: 786c2673f26a627f8b28edd297319032 (MD5) / Universidade Federal Fluminense. Instituto de Química. Programa de Pós-Graduação em Geoquímica, Niterói, RJ / rio Campo Belo localizado na vertente sul do maciço de Itatiaia (RJ), drena uma área de 90 km2. Foi amostrado mensalmente de Setembro de 2003 a Agosto de 2004. As coletas foram realizadas entre 400 a 510 m de altitude. Três pontos amostrados foram estabelecidos nas seguintes alturas: 510 m (ponto I), 410 m (ponto II) e 400 m (ponto III). Acima de 600 m, a bacia é coberta por vegetação nativa (Floresta Tropical). Abaixo de 500 m, a bacia é muito influenciada por atividades humanas (pasto e urbanização). As águas do rio Campo Belo apresentam baixa condutividade (média, 14,9 μS cm-1) e ligeiramente ácida (média pH,5,7). O conteúdo médio de N total nas águas fluviais foi distribuído em 12 % de NH4 +, 34 % NO3 -, 34 % NOP e 17 % de NOD. O conteúdo de P total foi distribuído em 3 % de PO4 3-, 55 % de POP e 43 % de POD. Então, as formas orgânicas predominam nas águas do rio Campo Belo. O intemperismo tem influencia trivial nas concentrações das águas fluviais. Quantidades significativas de Na, K, Ca e Mg tem sua origem nos minerais nefelina, piroxênio e anfibólio, predominante na região. Os fluxos de saída dos parâmetros analisados apresentaram valores máximos no período de chuvas intensas, reflexo do intemperismo mais intenso provocado pelas chuvas e escoamento superficial do material depositado no solo. A vazão apresentou uma variação sazonal característica, apresentando pico em dezembro e janeiro. / The Campo Belo river is located in the southern flank of the Itatiaia (RJ), drains an 90 km2 watershed, was sampled monthly from September 2003 to August 2004. The three sampling sites were established at the following heights: 510 m (site I), 410 m (site II) and 400 m (site III), Above 600 m height, the watershed is covered by native vegetation (tropical forest). Below 500 m, the watershed is quite influenced by human activities (pastures and urbanization). The water of the Campo Belo river has low conductivity (mean, 14,9 μS cm-1) and is slightly acidic (mean pH, 5,7). The total N content in river water was distributed as follows 12 % de NH4 +, 34 % NO3 -, 34 % PON e 17 % de DON. The total P content in river water was distributed as follows em 3 % de PO4 3-, 55 % de POP e 43 % de DOP. So, organic was the dominat form of N and mainly P in water. Weathering has a trivial influence on the river water. Significant amounts of Na, K, Ca and Mg has origin on the mineral nepheline, pyroxenes and amphiboles predominated in the region. The stream water export of parameters searched showing maximum values during rainfall, reflex weathering and runoff of material deposited on soil. Discharge presented a characteristic seasonal variation, showing a peak in December and January.

River export has a strong influence on the productivity of coastal waters. During storm events, rivers deliver disproportionate amounts of nutrients and organic matter to estuaries. Anthropogenic changes to the land use/cover (LULC) and water use also have a strong influence on the export of nutrients and organic matter to estuaries. This study specifically addressed the following questions: 1) How does river water chemistry vary across LULC patterns in the Mission and Aransas river watersheds? 2) How do fluxes of water, nutrients, and organic matter in the rivers vary between base flow and storm flow? 3) How do variations in nutrient/organic matter concentrations and stable isotope ratios of particulate organic matter (POM) in Copano Bay relate to river inputs? Water was collected from the Mission and Aransas rivers and Copano Bay from July, 2007 through November, 2008 and analyzed for concentrations of nitrate, ammonium, soluble reactive phosphorus (SRP), dissolved organic nitrogen, dissolved organic carbon, particulate organic nitrogen, particulate organic carbon (POC), and the stable C and N isotope ratios of the POM. The first half of the study period captured relatively wet conditions and the second half was relatively dry compared to long term climatology. Riverine export was calculated using the USGS LOADEST model. The percentage of annual constituent export during storms in 2007 was much greater than in 2008. Concentration-discharge relationships for inorganic nutrients varied between rivers, but concentrations were much higher in the Aransas River due to waste water contributions. Organic matter concentrations increased with flow in both rivers, but POM concentrations in the Aransas River were two fold higher due to large percentages of cultivated crop land. Values of [delta]¹³C-POC show a shift from autochthonous to allochthonous organic matter during storm events. Following storm events in Copano Bay, increases and quick draw down of nitrate and ammonium concentrations coupled with increases and slow draw down of SRP illustrate nitrogen limitation. Organic matter concentrations remained elevated for ~9 months following storm events. The [delta]¹³C-POC data show that increased concentrations were specifically related to increased autochthonous production. Linkages between LULC and nutrient loading to coastal waters are widely recognized, but patterns of nutrient delivery (i.e. timing, duration, and magnitude of watershed export) are often not considered. This study demonstrates the importance of sampling during storm events and defining system-specific discharge-concentration relationships for accurate watershed export estimation. This study also shows that storm inputs can support increased production for extended periods after events. Consideration of nutrient delivery patterns in addition to more traditional studies of LULC effects would support more effective management of coastal ecosystems in the future. / text

Solid-phase microextraction (SPME) coupled with gas chromatography/mass spectrometry (GC/MS) is routinely used to analyze polycyclic aromatic hydrocarbons (PAHs) in water. A common SPME-GC/MS approach quantifies target analytes using isotopically labeled standards (IISs); one IIS is needed for each target analyte. This approach is challenging, even prohibitive since IISs are often expensive; moreover, they are generally not available for each analyte of interest. This study developed a novel SPME-GC/MS approach for the quantification of PAHs in water. The new method, which employs only a small number of IISs, uses relative response factor (RRF) (i.e., analyte corresponding to IIS) to quantify PAHs in water. Possible matrix dependency of RRFs values was examined using water that was modified concerning different physical-chemical characteristics (i.e., ionic strength, pH, suspended solids, humic acid, and biological organic carbon represented by hemoglobin). The results revealed that RRFs are not noticeably affected by changing ionic strength and pH; the other three parameters did affect the RRFs. However, the results also showed that the effect is minimal when the solution is dilute (i.e., low concentrations of suspended solids, humic acid or hemoglobin). Relatively stable RRFs for dilute water solutions indicates that this approach can be used for routine quantification of water that does not contain prohibitive amounts of suspended solids, humic acid, and biological organic matter. The developed method was employed to quantify trace levels of PAHs in three different types of water, namely river water, well water, and bottled water. PAH levels in every kind of water were less than 100 ng/L level (i.e., 0.1 ppb). Analyses of spiked water samples containing 2 ng PAHs revealed correlations between calculated RRFs and the physical-chemical properties of the PAHs investigated (i.e., vapor pressure, boiling point, octanol/water partition coefficient, octanol/air partition coefficient, GC retention time). This implies that RRFs for PAHs not examined in this study can be predicted. Overall, the results presented herein constitute a meaningful contribution to the development of SPME-GC/MS methods for quantitative analysis of PAHs and other chemicals in dilute aqueous solutions. Moreover, the development of methods that alleviate the need for IISs corresponding to each target analyte.

Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variables (e.g., temperature, humidity, mean sea level pressure etc.) to hydrologic variables (e.g., precipitation and streamflow), at regional scale. Statistical downscaling methods are generally used in the hydrological impact assessment studies for downscaling climate projections provided by the General Circulation Models (GCMs). GCMs are climate models designed to simulate time series of climate variables globally, accounting for the greenhouse gases in the atmosphere. The statistical techniques used to bridge the spatial and temporal resolution gaps between what GCMs are currently able to provide and what impact assessment studies require are called as statistical downscaling methods. Generally, these methods involve deriving empirical relationships that transform large-scale simulations of climate variables (referred as the predictors) provided by a GCM to regional scale hydrologic variables (referred as the predictands). This general methodology is characterized by various uncertainties such as GCM and scenario uncertainty, uncertainty due to initial conditions of the GCMs, uncertainty due to downscaling methods, uncertainty due to hydrological model used for impact assessment and uncertainty resulting from multiple stake holders in a water resources system. The research reported in this thesis contributes towards (i) development of methodologies for climate change impact assessment of various components of a water resources system, such as water quality, water availability, irrigation and reservoir operation, and (ii) quantification of GCM and scenario uncertainties in hydrologic impacts of climate change. Further, an integrated reservoir operation model is developed to derive optimal operating policies under the projected scenarios of water availability, irrigation water demands, and water quality due to climate change accounting for various sources of uncertainties. Hydropower generation is also one of the objectives in the reservoir operation. The possible climate change impact on river water quality is initially analyzed with respect to hypothetical scenarios of temperature and streamflow, which are affected by changes in precipitation and air temperature respectively. These possible hypothetical scenarios are constructed for the streamflow and river water temperature based on recent changes in the observed data. The water quality response is simulated, both for the present conditions and for conditions resulting from the hypothetical scenarios, using the water quality simulation model, QUAL2K. A Fuzzy Waste Load Allocation Model (FWLAM) is used as a river water quality management model to derive optimal treatment levels for the dischargers in response to the hypothetical scenarios of streamflow and water temperature. The scenarios considered for possible changes in air temperature (+1 oC and +2 oC) and streamflow (-0%, -10%, -20%) resulted in a substantial decrease in the Dissolved Oxygen (DO) levels, increase in Biochemical Oxygen Demand (BOD) and river water temperature for the case study of the Tunga-Bhadra River, India. The river water quality indicators are analyzed for the hypothetical scenarios when the BOD of the effluent discharges is at safe permissible level set by Pollution Control Boards (PCBs). A significant impairment in the water quality is observed for the case study, under the hypothetical scenarios considered. A multi-variable statistical downscaling model based on Canonical Correlation Analysis (CCA) is then developed to downscale future projections of hydro¬meteorological variables to be used in the impact assessment study of river water quality. The CCA downscaling model is used to relate the surface-based observations and atmospheric variables to obtain the simultaneous projection of hydrometeorological variables. Statistical relationships in terms of canonical regression equations are obtained for each of the hydro-meteorological predictands using the reanalysis data and surface observations. The reanalysis data provided by National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used for the purpose. The regression equations are applied to the simulated GCM output to model future projections of hydro-meteorological predictands. An advantage of the CCA methodology in the context of downscaling is that the relationships between climate variables and the surface hydrologic variables are simultaneously expressed, by retaining the explained variance between the two sets. The CCA method is used to model the monthly hydro-meteorological variables in the Tunga-Bhadra river basin for water quality impact assessment study. A modeling framework of risk assessment is developed to integrate the hydro¬meteorological projections downscaled from CCA model with a river water quality management model to quantify the future expected risk of low water quality under climate change. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold DO level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is adopted to evaluate the future fractional removal policies for each of the dischargers by including the predicted future risk levels. The hydro-meteorological projections of streamflow, air temperature, relative humidity and wind speed are modeled using MIROC 3.2 GCM simulations with A1B scenario. The river water temperature is modeled by using an analytical temperature model that includes the downscaled hydro-meteorological variables. The river water temperature is projected to increase under climate change, for the scenario considered. The IFWLAM uses the downscaled projections of streamflow, simulated river water temperature and the predicted lower and upper future risk levels to determine the fraction removal policies for each of the dischargers. The results indicate that the optimal fractional removal levels required for the future scenarios will be higher compared to the present levels, even if the effluent loadings remain unchanged. Climate change is likely to impact the agricultural sector directly with changes in rainfall and evapotranspiration. The regional climate change impacts on irrigation water demands are studied by quantifying the crop water demands for the possible changes of rainfall and evapotranspiration. The future projections of various meteorological variables affecting the irrigation demand are downscaled using CCA downscaling model with MIROC 3.2 GCM output for the A1B scenario. The future evapotranspiration is obtained using the Penman-Monteith evapotranspiration model accounting for the projected changes in temperature, relative humidity, solar radiation and wind speed. The monthly irrigation water demands of paddy, sugarcane, permanent garden and semidry crops quantified at nine downscaling locations covering the entire command area of the Bhadra river basin, used as a case study, are projected to increase for the future scenarios of 2020-2044, 2045-2069 and 2070-2095 under the climate change scenario considered. The GCM and scenario uncertainty is modeled combinedly by deriving a multimodel weighted mean by assigning weights to each GCM and scenario. An entropy objective weighting scheme is proposed which exploits the information contained in various GCMs and scenarios in simulating the current and future climatology. Three GCMs, viz., CGCM2 (Meteorological Research Institute, Japan), MIROC3.2 medium resolution (Center for Climate System Research, Japan), and GISS model E20/Russell (NASA Goddard Institute for Space Studies, USA) with three scenarios A1B, A2 and B1 are used for obtaining the hydro-meteorological projections for the Bhadra river basin. Entropy weights are assigned to each GCM and scenario based on the performance of the GCM and scenario in reproducing the present climatology and deviation of each from the projected ensemble average. The proposed entropy weighting method is applied to projections of the hydro-meteorological variables obtained based on CCA downscaling method from outputs of the three GCMs and the three scenarios. The multimodel weighted mean projections are obtained for the future time slice of 2020-2060. Such weighted mean hydro-meteorological projections may be further used into the impact assessment model to address the climate model uncertainty in the water resources systems. An integrated reservoir operation model is developed considering the objectives of irrigation, hydropower and downstream water quality under uncertainty due to climate change, uncertainty introduced by fuzziness in the goals of stakeholders and uncertainty due to the random nature of streamflow. The climate model uncertainty originating from the mismatch between projections from various GCMs under different scenarios is considered as first level of uncertainty, which is modeled by using the weighted mean hydro-meteorological projections. The second level of uncertainty considered is due to the imprecision and conflicting goals of the reservoir users, which is modeled by using fuzzy set theory. A Water Quantity Control Model (WQCM) is developed with fuzzy goals of the reservoir users to obtain water allocations among the different users of the reservoir corresponding to the projected demands. The water allocation model is updated to account for the projected demands in terms of revised fuzzy membership functions under climate change to develop optimal policies of the reservoir for future scenarios. The third level of uncertainty arises from the inherent variability of the reservoir inflow leading to uncertainty due to randomness, which is modeled by considering the reservoir inflow as a stochastic variable. The optimal monthly operating polices are derived using Stochastic Dynamic Programming (SDP), separately for the current and for the future periods of 2020-2040 and 2040-2060 The performance measures for Bhadra reservoir in terms of reliability and deficit ratios for each reservoir user (irrigation, hydropower and downstream water quality) are estimated with optimal SDP policy derived for current and future periods. The reliability with respect to irrigation, downstream water quality and hydropower show a decrease for 2020-2040 and 2040-2060, while deficit ratio increases for these periods. The results reveal that climate change is likely to affect the reservoir performance significantly and changes in the reservoir operation for the future scenarios is unable to restore the past performance levels. Hence, development of adaptive responses to mitigate the effects of climate change is vital to improve the overall reservoir performance.

Electricity markets may become more sensitive to weather conditions because of higher penetration of renewable energy sources and climatic changes. Albania is 100% reliant on hydropower for its domestic energy generation, making this country compelling to investigate as it is highly sensitive to changing weather conditions. We use an ARMA-GARCH model to investigate whether weather and economic factors had a relationship with monthly hydroelectricity prices in the Albanian Energy Market in the period 2013-2018. We find that electricity price is affected by variations in weather and is not utterly robust to extreme hydrological changes. Generally, our dependent variable appears to be particularly influenced by air pressure followed by temperature and rainfall. We also perceive that there is a relationship between economic factors and hydroelectricity prices, where residual supply appears to have a significant negative relationship with our dependent variable. However, we were originally anticipating a higher dependency of electricity prices on weather conditions, due to the inflated hydro-power reliance for electricity production in the Albanian Energy Market. This effect is offset by several factors, where the state monopolized behaviour of the energy sector occupies a predominant influence on our results.

This Diploma Thesis is focused on study of electrochemical behavior of 2,4,6-trinitrotoluene (TNT) and 2,4,6-trinitrophenol (picric acid) on finding the optimum conditions for their determination using direct current voltammetry (DCV) and differential pulse voltammetry (DPV) at a mercury meniscus modified silver solid amalgam electrode (m-AgSAE) in the solution of Britton-Robinson (BR) and on finding of the limit of quantification (LQ) for these substances. Practical applicability of the newly developed methods was verified on direct determination of TNT and picric acid in model samples of drinking and river water. Moreover, the electrochemical behaviors of TNT and picric acid was studied using cyclic voltammetry (CV). Optimum medium for the determination of TNT at m-AgSAE was: methanol-BR buffer pH 4.0 (1:9). Upon the DCV it is proper to apply regeneration potentials Ereg,1= 0 mV and Ereg,2= -1100 mV and upon the DPV was apply regeneration potentials Ereg,1= 0 mV and Ereg,2= -600 mV were applied. The concentration dependence of the peak current was found to be linear for both techniques over the concentration range of 1·10-6 -1·10-4 mol/l with LQ of 0.54 µmol/l (for DCV) and 0.46 µmol/l (for DPV). The method developed for the determination of TNT were verified on the model samples of drinking...

The aim of presented Diploma Thesis was to study an electrochemical behavior of nitroimidazole drugs metronidazole and ornidazole and to find optimal conditions for their voltammetric determination at a mercury meniscus modified silver solid amalgam electrode using DC voltammetry (DCV) and differential pulse voltammetry (DPV). Voltammetric behavior of selected drugs was investigated in dependence on the pH of the medium used (realized using a Britton-Robinson buffer (BR buffer)) and a mechanism of the reduction of both drugs was investigated using cyclic voltammetry (CV). The optimum medium for voltammetric determination of studied nitroimidazole drugs at the m-AgSAE in a region of cathodic potentials was found to be the BR buffer of pH 8.0. Then, the concentration dependences were measured in this optimum medium in the concentration range from 2·10-7 mol/L to 1·10-4 mol/L. The limits of quantification (LQs) for both metronidazole and ornidazole were found in the concentration order of 10-7 mol/L by using DCV and DPV at the m-AgSAE. The applicability of the newly developed voltammetric methods of the determination of nitroimidazole drugs was verified on the model samples of drinking and river water, with LQ ≈ 2·10-7 mol/L for both DC voltammetry and differential pulse voltammetry at the m-AgSAE....

Waste Load Allocation (WLA) in rivers refers to the determination of required pollutant fractional removal levels at a set of point sources of pollution to ensure that water quality standards are maintained throughout the system. Optimal waste load allocation implies that the selected pollution treatment vector not only maintains the water quality standards, but also results in the best value for the objective function defined for the management problem. Waste load allocation problems are characterized by uncertainties due to the randomness and imprecision. Uncertainty due to randomness arises mainly due to the random nature of the variables influencing the water quality. Uncertainty due to imprecision or fuzziness is associated with setting up the water quality standards and goals of the Pollution Control Agencies (PCA), and the dischargers (e.g., industries and municipal dischargers). Many decision problems in water resources applications are dominated by natural, extreme, rarely occurring, uncertain events. However usually such events will be absent or be rarely present in the historical records. Due to the scarcity of information of these uncertain events, a realistic decision-making becomes difficult. Furthermore, water resources planners often deal with imprecision, mostly due to imperfect knowledge and insufficient or inadequate data. Therefore missing data is very common in most water resources decision problems. Missing data introduces inaccuracy in analysis and evaluation. For instance, the sample mean of the available data can be an inaccurate estimate of the mean of the complete data. Use of sample statistics estimated from inadequate samples in WLA models would lead to incorrect decisions. Therefore there is a necessity to incorporate the uncertainty due to missing data also in WLA models in addition to the uncertainties due to randomness and imprecision. The uncertainty in the input parameters due to missing or inadequate data renders the input parameters (such as mean and variance) as interval grey parameters in water quality decision-making. In a Fuzzy Waste Load Allocation Model (FWLAM), randomness and imprecision both can be addressed simultaneously by using the concept of fuzzy risk of low water quality (Mujumdar and Sasikumar, 2002). In the present work, an attempt is made to also address uncertainty due to partial ignorance due to missing data or inadequate data in the samples of input variables in FWLAM, considering the fuzzy risk approach proposed by Mujumdar and Sasikumar (2002). To address the uncertainty due to missing data or inadequate data, the input parameters (such as mean and variance) are considered as interval grey numbers. The resulting output water quality indicator (such as DO) will also, consequently, be an interval grey number. The fuzzy risk will also be interval grey number when output water quality indicator is an interval grey number. A methodology is developed for the computation of grey fuzzy risk of low water quality, when the input variables are characterized by uncertainty due to partial ignorance resulting from missing or inadequate data in the samples of input variables. To achieve this, an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system to address uncertainties due to randomness, fuzziness and also due to missing data or inadequate data. Monte Carlo Simulation (MCS) incorporating a water quality simulation model is performed two times for each set of randomly generated input variables: once for obtaining the upper bound of DO and once for the lower bound of DO, by using appropriate upper or lower bounds of interval grey input variables. These two bounds of DO are used in the estimation of grey fuzzy risk by substituting the upper and lower values of fuzzy membership functions of low water quality. A backward finite difference scheme (Chapra, 1997) is used to solve the water quality simulation model. The goal of PCA is to minimize the bounds of grey fuzzy risk, whereas the goal of dischargers is to minimize the fractional removal levels. The two sets of goals are conflicting with each other. Fuzzy multiobjective optimization technique is used to formulate the multiobjective model to provide best compromise solutions. Probabilistic Global Search Lausanne (PGSL) method is used to solve the optimization problem. Finally the results of the model are compared with the results of risk minimization model (Ghosh and Mujumdar, 2006), when the methodology is applied to the case study of the Tunga-Bhadra river system in South India. The model is capable of determining a grey fuzzy risk with the corresponding bounds of DO, at each check point, rather than specifying a single value of fuzzy risk as done in a Fuzzy Waste Load Allocation Model (FWLAM). The IFWLAM developed is based on fuzzy multiobjective optimization problem with ‘max-min’ as the operator, which usually may not result in a unique solution and there exists a possibility of obtaining multiple solutions (Karmakar and Mujumdar, 2006b). Karmakar and Mujumdar (2006b) developed a two-phase Grey Fuzzy Waste Load Allocation Model (two-phase GFWLAM), to determine the widest range of interval-valued optimal decision variables, resulting in the same value of interval-valued optimal goal fulfillment level as obtained from GFWLAM (Karmakar and Mujumdar 2006a). Following Karmakar and Mujumdar (2006b), two optimization models are developed in this study to capture all the decision alternatives or multiple solutions: one to maximize and the other to minimize the summation of membership functions of the dischargers by keeping the maximum goal fulfillment level same as that obtained in IFWLAM to obtain a lower limit and an upper limit of fractional removal levels respectively. The aim of the two optimization models is to obtain a range of fractional removal levels for the dischargers such that the resultant grey fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision-making. The models are applied to the case study of Tunga-Bhadra river system. A range of upper and lower limits of fractional removal levels is obtained for each discharger; within this range, the discharger can select the fractional removal level so that the resulting grey fuzzy risk will also be within specified bounds. In IFWLAM, the membership functions are subjective, and lower and upper bounds are arbitrarily fixed. Karmakar and Mujumdar (2006a) developed a Grey Fuzzy Waste Load Allocation Model (GFWLAM), in which uncertainty in the values of membership parameters is quantified by treating them as interval grey numbers. Imprecise membership functions are assigned for the goals of PCA and dischargers. Following Karmakar and Mujumdar (2006a), a Grey Optimization Model with Grey Fuzzy Risk is developed in the present study to address the uncertainty in the memebership functions of IFWLAM. The goals of PCA and dischargers are considered as grey fuzzy goals with imprecise membership functions. Imprecise membership functions are assigned to the fuzzy set of low water quality and fuzzy set of low risk. The grey fuzzy risk approach is included to account for the uncertainty due to missing data or inadequate data in the samples of input variables as done in IFWLAM. Randomness and imprecision associated with various water quality influencing variables and parameters of the river system are considered through a Monte-Carlo simulation when input parameters (such as mean and variance) are interval grey numbers. The model application is demonstrated with the case study of Tunga-Bhadra river system in South India. Finally the results of the model are compared with the results of GFWLAM (Karmakar and Mujumdar, 2006a). For the case study of Tunga Bhadra River system, it is observed that the fractional removal levels are higher for Grey Optimization Model with Grey Fuzzy Risk compared to GFWLAM (Karmakar and Mujumdar, 2006a) and therefore the resulting risk values at each check point are reduced to a significant extent. The models give a set of flexible policies (range of fractional removal levels). Corresponding optimal values of goal fulfillment level and the grey fuzzy risk are all in terms of interval grey numbers. The IFWLAM and Grey Fuzzy Optimization Model with Grey Fuzzy Risk, developed in the study do not limit their application to any particular pollutant or water quality indicator in the river system. Given appropriate transfer functions for spatial distribution of the pollutants in water body, the models can be used for water quality management of any general river system.

From the Proceedings of the 1974 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 19-20, 1974, Flagstaff, Arizona / In the lower reaches of the Colorado River, damages from the increase in salinity to U.S. water users are now estimated to be about 53 million dollars per year and will increase to about 124 million dollars per year by the year 2000 if no salinity control measures are taken. Physical, legal, economic, and institutional aspects of the salinity problem and proposed actions to mesh salinity control with a total water management plan for the basin are discussed. A scheme is presented for planning under the Colorado River water quality improvement program. Recent legislative action is also discussed which provides control plans to improve the water quality delivered to Mexico as well as upper basin water users. These efforts now under study will assure the continued, full utility of Colorado River water to U.S. users and Mexico. However, more extensive development of the basin's natural resources puts new emphasis on total resources management through improved water and land use planning to conserve a most precious western resource - water.